Uncertainty Estimation for Molecules: Desiderata and Methods
Authors: Tom Wollschläger, Nicholas Gao, Bertrand Charpentier, Mohamed Amine Ketata, Stephan Günnemann
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our extensive experimental evaluation, we test four different UE with three different backbones and two datasets. In out-of-equilibrium detection, we find LNK yielding up to 2.5 and 2.1 times lower errors in terms of AUC-ROC score than dropout or evidential regression-based methods while maintaing high predictive performance. |
| Researcher Affiliation | Academia | Tom Wollschl ager 1 Nicholas Gao 1 Bertrand Charpentier 1 Mohamed Amine Ketata 1 Stephan G unnemann 1 1Department of Computer Science & Munich Data Science Institute, Technical University of Munich, Germany. |
| Pseudocode | No | The paper does not contain any explicitly labeled pseudocode or algorithm blocks. Methods are described textually. |
| Open Source Code | Yes | Find our code at cs.cit.tum.de/daml/uncertainty-for-molecules |
| Open Datasets | Yes | Datasets. QM7-X: (Hoja et al., 2021) This dataset covers both equilibrium and non-equilibrium structures. We train on equilibrium structures and non-equilibrium structures are considered OOD data. MD17: (Chmiela et al., 2017) MD17 contains energies and forces for molecular dynamics trajectories of different organic molecules. |
| Dataset Splits | Yes | Table 10. Hyperparameters of the datasets used with all models: val set size 4151 1000 |
| Hardware Specification | No | The paper mentions evaluating runtime on QM7X but does not provide specific details about the hardware used, such as GPU or CPU models, or cloud computing instance types. |
| Software Dependencies | No | The paper mentions using PyTorch-Geometric in the footnote of Table 11 for Sch Net, but it does not specify version numbers for PyTorch-Geometric, PyTorch, CUDA, or other key software components, which is necessary for reproducibility. |
| Experiment Setup | Yes | Table 8 and Table 9 provide specific hyperparameters and settings used for training models on QM7-X and MD17 datasets, including learning rate, patience, force weighting factor, number of inducing points, warmup steps, decay steps, decay rate, EMA decay, and dropout locations. |